5 Top Data Science Skills and Tools

Data science professionals are in demand, with the U.S. Bureau of Labor Statistics (BLS) projecting an astonishing 34% job growth for data science occupations between 2024 and 2034.

Individuals in the field use their data science skills to analyze data in order to uncover valuable insights. They apply data science tools and specialized software to organize and analyze both structured and unstructured data, and make communicating what the data reveals easier and more effective.

Proficiency with artificial intelligence (AI) technology is among the data science skills that employers are currently seeking. Research from the U.S. Census Bureau shows that when new technologies reshape the work of professionals in science, technology, engineering, and math (STEM) fields, it typically leads to those professionals needing to strengthen rather than replace their existing capabilities. In 2023, about 22% of workers reported that AI tools enhanced their skills and increased their value.

For individuals ready to take the next step in their data science career, earning a Master of Science in Data Science can help them build their data science skills through flexible learning.

5 Key Data Scientist Skills and Tools

When pursuing a career in data science, individuals need to identify which data scientist skills are most vital for their success. They also need to identify which data science tools are indispensable. This short list explores five key data scientist skills and tools and discusses why they’re important.

1. Understanding of the Data Science Process

Data science involves collecting and cleaning data and then exploring, modeling, and interpreting it. Following these steps helps professionals ensure the data’s accuracy, reveal patterns, and build effective predictive models. Mastering this process is essential because it allows professionals to turn raw information into actionable insights that drive real-world decisions.

2. Knowledge of Different Types of Analytics

Data analytics takes four forms, each of which is applied for different purposes but works toward the same goal: informed decision-making.

• Descriptive analytics lays the groundwork by summarizing past data, providing a clear understanding of what has happened.
• Diagnostic analytics uncovers patterns and root causes in the data to explain why things occurred.
• Predictive analytics uses historical data to anticipate what’s ahead, forecasting likely outcomes and potential risks.
• Prescriptive analytics recommends specific actions to optimize results and drive performance.

3. Communication, Presentation, and Data Visualization Skills

Data scientists use their communication and visualization skills to help decision-makers clearly understand the results of their analysis. They do this by transforming raw data into stories that teams can act on. Tools like Tableau make it easier for data professionals to create interactive charts, graphs, and dashboards that help them share their knowledge to guide informed decisions.

4. Mathematical and Quantitative Competencies

Mathematics and quantitative reasoning skills allow data scientists to analyze data sets, identify trends, and translate findings into measurable results — such as by tracking a product’s quality against its defect rates. Tools such as Matlab for numerical computing and IBM SPSS and SAS for statistical analysis support this key area of data scientists’ work.

5. Programming Languages and Data Science Tools

Programming languages and tools other than those mentioned above that are used by data scientists to manage data and develop models include the following:

• Python: Known for its flexibility, ease of use, and machine learning libraries, Python is a go-to programming language for all types of data science uses, from quick data cleaning to advanced AI projects.
• R: Designed for statistical computing and visualization, with C++ integration for intensive tasks, R is ideal for projects requiring deep analysis and clear graphics.
• SQL: The standard for querying and managing databases, SQL is essential because nearly every organization relies on database-driven information.
• DuckDB: A lightweight tool, DuckDB helps data professionals optimize analytical queries by offering the power of a conventional database without heavy setup.
• ConnectorX: ConnectorX helps data scientists handle large data sets efficiently by speeding up data transfers from databases into Python.
• Hadoop: Hadoop processes large data sets across distributed systems, making it useful when there is too much data for a single machine to handle.

Benefits of Earning an Advanced Data Science Degree

In addition to preparing individuals with essential data science skills, earning an advanced degree can open doors for professionals to new opportunities, enhance their career prospects, and equip them to make a meaningful impact.

Developing Expertise

Earning an advanced degree helps students master the core programming languages essential for data science and machine learning applications, and provides opportunities to perform statistical modeling through hands-on projects. This powerful blend of theory and practice prepares individuals to handle complex problem-solving and real-world data science challenges.

Staying Ahead in a Competitive Job Market

The volume of data worldwide is expected to exceed 200 zettabytes in 2025, according to Cybercrime Magazine. As a result, the demand for data science professionals who can interpret and leverage this data to drive business decisions is likely to continue to skyrocket, making earning an advanced data science degree a solid investment for individuals looking to help meet this demand.

Making a Difference

Data scientists can create a lasting societal impact in a number of industries. The National Institutes of Health (NIH) STRIDES Initiative, for example, gives researchers affordable access to the tools and resources needed to work with large data sets to advance biomedical research. Earning an advanced data science degree can prepare individuals with versatile skills that can enable them to contribute to these world-changing efforts.

Improve Your Data Science Skills to Grow Your Career

A foundation of data science skills, including capabilities working with tools, programming languages, analytics, and communication, is essential for individuals considering advancing in their data science careers. Earning a graduate degree not only helps individuals strengthen these skills, it also prepares them to take on complex challenges, stay competitive in a fast-changing job market, and contribute to initiatives that create lasting impact.

The online Master of Science in Data Science program at Tseng College at California State University, Northridge, helps prepare students to advance in their careers with a 10-course curriculum that provides a well-rounded blend of theoretical foundations and hands-on practice.

Students learn Python programming for data science and analytics and choose electives from topics such as database design, machine learning, data visualization, and deep learning. Students have opportunities to apply their skills in real-world scenarios throughout their enrollment and demonstrate how to use data science to help solve large-scale real-world problems in their final project.

Find out how the data science program can help you grow your career.